% Key papers for Confidence calibration objectives (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2019_why_15033,
  title = {{Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2019_on_58713,
  title = {{On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14319},
}

@inproceedings{cvpr2020_uncertainty_16397,
  title = {{Uncertainty-Aware Score Distribution Learning for Action Quality Assessment}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2019_striking_14876,
  title = {{Striking the Right Balance With Uncertainty}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{iclr2018_training_28723,
  title = {{Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/225},
}

@inproceedings{cvpr2023_deep_20736,
  title = {{Deep Deterministic Uncertainty: A New Simple Baseline}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21132},
}

@inproceedings{neurips2020_calibrating_59504,
  title = {{Calibrating Deep Neural Networks using Focal Loss}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18824},
}

@inproceedings{iclr2020_pitfalls_29320,
  title = {{Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BJxI5gHKDr},
}

@inproceedings{cvpr2022_the_20009,
  title = {{The Devil Is in the Margin: Margin-Based Label Smoothing for Network Calibration}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2021_post_17637,
  title = {{Post-Hoc Uncertainty Calibration for Domain Drift Scenarios}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{icml2019_addressing_37138,
  title = {{Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3769},
}

@inproceedings{icml2020_confidence_38035,
  title = {{Confidence-Aware Learning for Deep Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6730},
}

@inproceedings{cvpr2022_a_18215,
  title = {{A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{aaai2022_robust_2655,
  title = {{Robust Depth Completion with Uncertainty-Driven Loss Functions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/03626-robust-depth-completion-with-uncertainty-driven-loss-functions/},
}

@inproceedings{neurips2021_rethinking_62054,
  title = {{Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28134},
}

@inproceedings{aaai2023_multi_4196,
  title = {{Multi-Level Confidence Learning for Trustworthy Multimodal Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26346},
}

@inproceedings{cvpr2020_auxiliary_15173,
  title = {{Auxiliary Training: Towards Accurate and Robust Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{iclr2021_multi_30082,
  title = {{Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2923},
}

@inproceedings{aaai2023_post_4391,
  title = {{Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26167},
}

@inproceedings{cvpr2022_calibrating_18433,
  title = {{Calibrating Deep Neural Networks by Pairwise Constraints}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2024_towards_25035,
  title = {{Towards Calibrated Multi-label Deep Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29602},
}

@inproceedings{aaai2024_suppressing_7445,
  title = {{Suppressing Uncertainty in Gaze Estimation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28368},
}

@inproceedings{cvpr2024_data_23112,
  title = {{Data-Free Quantization via Pseudo-label Filtering}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30205},
}

@inproceedings{icml2024_estimating_44742,
  title = {{Estimating Canopy Height at Scale}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33710},
}

@inproceedings{neurips2024_lascal_72296,
  title = {{LaSCal: Label-Shift Calibration without target labels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95049},
}

@inproceedings{aaai2025_risk_10565,
  title = {{Risk Controlled Image Retrieval}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34931},
}

@inproceedings{neurips2024_multi_71521,
  title = {{Multi-model Ensemble Conformal Prediction in Dynamic Environments}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95742},
}

@inproceedings{neurips2024_conformal_72290,
  title = {{Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95055},
}

@inproceedings{neurips2024_toward_72389,
  title = {{Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94964},
}

@inproceedings{neurips2024_confidence_73175,
  title = {{Confidence Calibration of Classifiers with Many Classes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94258},
}

@inproceedings{neurips2024_hierarchical_74464,
  title = {{Hierarchical Selective Classification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93110},
}

@inproceedings{aaai2025_uncertainty_11137,
  title = {{Uncertainty-Aware Self-Training for CTC-Based Automatic Speech Recognition}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34610},
}

@inproceedings{cvpr2024_self_24781,
  title = {{Self-Calibrating Vicinal Risk Minimisation for Model Calibration}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30606},
}

@inproceedings{cvpr2025_improving_26602,
  title = {{Improving Accuracy and Calibration via Differentiated Deep Mutual Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33883},
}

@inproceedings{icml2024_how_43385,
  title = {{How Flawed Is ECE? An Analysis via Logit Smoothing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35067},
}

@inproceedings{cvpr2025_balancing_25567,
  title = {{Balancing Two Classifiers via A Simplex ETF Structure for Model Calibration}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33254},
}

@inproceedings{icml2024_adaptive_45096,
  title = {{Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33356},
}

@inproceedings{aaai2026_minimum_107746,
  title = {{Minimum-Length Conformal Prediction Sets for Ordinal Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40098},
}

@inproceedings{aaai2025_ensuring_8997,
  title = {{Ensuring Class-Conditional Coverage for Pathological Workflows (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35279},
}

@inproceedings{aaai2025_generalizing_9324,
  title = {{Generalizing Constraint Models in Constraint Acquisition}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33236},
}